Overview

Dataset statistics

Number of variables20
Number of observations295617
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 10 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 8 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 11 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -104.4702418)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32186 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:48:23.888967
Analysis finished2022-12-20 08:50:12.370371
Duration1 minute and 48.48 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295617
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45458.056
Minimum0
Maximum90909.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:12.483219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4548.196
Q122720.78
median45461.802
Q368192.38
95-th percentile86375.674
Maximum90909.77
Range90909.77
Interquartile range (IQR)45471.6

Descriptive statistics

Standard deviation26246.34
Coefficient of variation (CV)0.57737489
Kurtosis-1.200327
Mean45458.056
Median Absolute Deviation (MAD)22735.841
Skewness-4.4726011 × 10-5
Sum1.3438174 × 1010
Variance6.8887037 × 108
MonotonicityStrictly increasing
2022-12-20T14:20:12.752146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60643.254 1
 
< 0.1%
60615.208 1
 
< 0.1%
60614.902 1
 
< 0.1%
60614.596 1
 
< 0.1%
60614.292 1
 
< 0.1%
60613.987 1
 
< 0.1%
60613.683 1
 
< 0.1%
60613.378 1
 
< 0.1%
60613.073 1
 
< 0.1%
Other values (295607) 295607
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.309 1
< 0.1%
0.618 1
< 0.1%
0.928 1
< 0.1%
1.237 1
< 0.1%
1.547 1
< 0.1%
1.856 1
< 0.1%
2.166 1
< 0.1%
2.474 1
< 0.1%
2.784 1
< 0.1%
ValueCountFrequency (%)
90909.77 1
< 0.1%
90909.461 1
< 0.1%
90909.151 1
< 0.1%
90908.843 1
< 0.1%
90908.534 1
< 0.1%
90908.227 1
< 0.1%
90907.917 1
< 0.1%
90907.608 1
< 0.1%
90907.299 1
< 0.1%
90906.99 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct299
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8978496
Minimum0
Maximum20
Zeros32186
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:12.922346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4275957
Coefficient of variation (CV)0.64939315
Kurtosis-1.2332917
Mean9.8978496
Median Absolute Deviation (MAD)6.67
Skewness0.0096019633
Sum2925972.6
Variance41.313987
MonotonicityNot monotonic
2022-12-20T14:20:13.091448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32186
10.9%
4.44 29261
9.9%
2.22 29251
9.9%
11.11 29240
9.9%
6.67 29240
9.9%
13.33 29239
9.9%
8.89 29237
9.9%
20 29226
9.9%
17.78 29225
9.9%
15.56 29222
9.9%
Other values (289) 290
 
0.1%
ValueCountFrequency (%)
0 32186
10.9%
0.1931 1
 
< 0.1%
0.2464 1
 
< 0.1%
0.2708 1
 
< 0.1%
0.4396 1
 
< 0.1%
0.4932 1
 
< 0.1%
0.5772 1
 
< 0.1%
0.8702 1
 
< 0.1%
0.8813 1
 
< 0.1%
0.928 1
 
< 0.1%
ValueCountFrequency (%)
20 29226
9.9%
19.8134 1
 
< 0.1%
19.7536 1
 
< 0.1%
19.56 1
 
< 0.1%
19.0987 1
 
< 0.1%
19.0765 1
 
< 0.1%
18.707 1
 
< 0.1%
18.6325 1
 
< 0.1%
18.5687 1
 
< 0.1%
18.4016 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct20174
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.918431
Minimum16.34
Maximum72.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:13.375111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.34
5-th percentile24.07
Q138.07
median47.96
Q357.14
95-th percentile66.54
Maximum72.61
Range56.27
Interquartile range (IQR)19.07

Descriptive statistics

Standard deviation12.682915
Coefficient of variation (CV)0.2703184
Kurtosis-0.77275946
Mean46.918431
Median Absolute Deviation (MAD)9.79
Skewness-0.19845348
Sum13869886
Variance160.85634
MonotonicityNot monotonic
2022-12-20T14:20:13.524164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.07 3455
 
1.2%
48.98 3347
 
1.1%
38.09 2934
 
1.0%
39.14 2867
 
1.0%
30.68 2606
 
0.9%
38.07 2402
 
0.8%
32.78 2381
 
0.8%
52.53 2336
 
0.8%
32.76 2028
 
0.7%
39.12 2021
 
0.7%
Other values (20164) 269240
91.1%
ValueCountFrequency (%)
16.34 29
 
< 0.1%
16.36 1
 
< 0.1%
16.4004 1
 
< 0.1%
16.5338 1
 
< 0.1%
16.5743 1
 
< 0.1%
16.7077 1
 
< 0.1%
16.7481 1
 
< 0.1%
16.8809 1
 
< 0.1%
16.91 141
< 0.1%
16.9369 1
 
< 0.1%
ValueCountFrequency (%)
72.61 92
< 0.1%
72.6099 1
 
< 0.1%
72.6096 1
 
< 0.1%
72.6093 1
 
< 0.1%
72.5762 1
 
< 0.1%
72.5176 1
 
< 0.1%
72.4332 1
 
< 0.1%
72.3745 1
 
< 0.1%
72.2901 1
 
< 0.1%
72.2315 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct6656
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.960761
Minimum24.46
Maximum25.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:13.690524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum24.46
5-th percentile24.54
Q124.62
median24.9
Q325.26
95-th percentile25.58
Maximum25.66
Range1.2
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.34795154
Coefficient of variation (CV)0.013939941
Kurtosis-1.1272518
Mean24.960761
Median Absolute Deviation (MAD)0.28
Skewness0.44668933
Sum7378825.4
Variance0.12107027
MonotonicityNot monotonic
2022-12-20T14:20:13.853706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.54 25690
 
8.7%
24.62 25169
 
8.5%
24.7 23946
 
8.1%
24.94 20655
 
7.0%
24.58 14960
 
5.1%
25.5 14166
 
4.8%
24.82 11645
 
3.9%
24.74 11380
 
3.8%
25.42 9315
 
3.2%
25.26 8833
 
3.0%
Other values (6646) 129858
43.9%
ValueCountFrequency (%)
24.46 431
0.1%
24.461 2
 
< 0.1%
24.4611 1
 
< 0.1%
24.4612 1
 
< 0.1%
24.4614 1
 
< 0.1%
24.4616 1
 
< 0.1%
24.462 1
 
< 0.1%
24.4621 1
 
< 0.1%
24.4624 1
 
< 0.1%
24.4626 1
 
< 0.1%
ValueCountFrequency (%)
25.66 2529
0.9%
25.6596 1
 
< 0.1%
25.6593 1
 
< 0.1%
25.6591 1
 
< 0.1%
25.659 2
 
< 0.1%
25.6587 1
 
< 0.1%
25.6586 1
 
< 0.1%
25.6584 1
 
< 0.1%
25.658 1
 
< 0.1%
25.6578 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11440
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94538
Minimum0
Maximum273.0725
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:14.020114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7564
Q1239.8954
median239.9717
Q3240.0451
95-th percentile240.182
Maximum273.0725
Range273.0725
Interquartile range (IQR)0.1497

Descriptive statistics

Standard deviation1.8882839
Coefficient of variation (CV)0.0078696407
Kurtosis12409.044
Mean239.94538
Median Absolute Deviation (MAD)0.0748
Skewness-104.47024
Sum70931933
Variance3.5656162
MonotonicityNot monotonic
2022-12-20T14:20:14.169251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9882 142
 
< 0.1%
239.993 142
 
< 0.1%
239.9704 140
 
< 0.1%
239.9825 139
 
< 0.1%
239.9787 139
 
< 0.1%
239.9772 137
 
< 0.1%
239.9695 136
 
< 0.1%
239.9927 135
 
< 0.1%
239.9479 133
 
< 0.1%
239.9883 133
 
< 0.1%
Other values (11430) 294241
99.5%
ValueCountFrequency (%)
0 9
< 0.1%
0.2216 1
 
< 0.1%
0.5189 1
 
< 0.1%
0.8152 1
 
< 0.1%
18.0912 1
 
< 0.1%
38.9312 1
 
< 0.1%
59.6364 1
 
< 0.1%
90.1918 1
 
< 0.1%
108.1963 1
 
< 0.1%
112.9647 1
 
< 0.1%
ValueCountFrequency (%)
273.0725 1
< 0.1%
271.2882 1
< 0.1%
263.7828 1
< 0.1%
261.2391 1
< 0.1%
258.8797 1
< 0.1%
257.86 1
< 0.1%
257.7136 1
< 0.1%
256.5685 1
< 0.1%
256.5341 1
< 0.1%
256.0137 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1766
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3550143
Minimum0.198
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:14.333527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.198
5-th percentile0.1992
Q10.2
median0.2
Q30.207
95-th percentile0.8988
Maximum0.9
Range0.702
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28845866
Coefficient of variation (CV)0.81252686
Kurtosis-0.20652908
Mean0.3550143
Median Absolute Deviation (MAD)0.0001
Skewness1.3368413
Sum104948.26
Variance0.083208396
MonotonicityNot monotonic
2022-12-20T14:20:14.499206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 147140
49.8%
0.898 19547
 
6.6%
0.899 11498
 
3.9%
0.199 6611
 
2.2%
0.201 5865
 
2.0%
0.1998 4481
 
1.5%
0.1994 4424
 
1.5%
0.1997 4421
 
1.5%
0.1991 4419
 
1.5%
0.1992 4416
 
1.5%
Other values (1756) 82795
28.0%
ValueCountFrequency (%)
0.198 1
 
< 0.1%
0.1981 2
 
< 0.1%
0.1984 2
 
< 0.1%
0.1987 1
 
< 0.1%
0.1989 1
 
< 0.1%
0.199 6611
2.2%
0.1991 4419
1.5%
0.1992 4416
1.5%
0.1993 4342
1.5%
0.1994 4424
1.5%
ValueCountFrequency (%)
0.9 21
< 0.1%
0.8999 41
< 0.1%
0.8998 47
< 0.1%
0.8997 26
< 0.1%
0.8996 36
< 0.1%
0.8995 36
< 0.1%
0.8994 24
< 0.1%
0.8993 47
< 0.1%
0.8992 40
< 0.1%
0.8991 33
< 0.1%

R1 (MOhm)
Real number (ℝ)

Distinct8464
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.233782
Minimum0.0339
Maximum117.8584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:14.669234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0339
5-th percentile0.0792
Q10.4345
median2.1651
Q330.9681
95-th percentile73.1111
Maximum117.8584
Range117.8245
Interquartile range (IQR)30.5336

Descriptive statistics

Standard deviation24.760164
Coefficient of variation (CV)1.4367226
Kurtosis0.73460142
Mean17.233782
Median Absolute Deviation (MAD)2.0831
Skewness1.3857407
Sum5094598.8
Variance613.06573
MonotonicityNot monotonic
2022-12-20T14:20:14.833389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.9383 767
 
0.3%
77.677 755
 
0.3%
75.0345 732
 
0.2%
73.7788 719
 
0.2%
75.6194 716
 
0.2%
74.3444 712
 
0.2%
79.7171 696
 
0.2%
73.1111 691
 
0.2%
76.3332 679
 
0.2%
79.0683 674
 
0.2%
Other values (8454) 288476
97.6%
ValueCountFrequency (%)
0.0339 2
< 0.1%
0.034 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0342 1
 
< 0.1%
0.0345 2
< 0.1%
0.0347 3
< 0.1%
0.0348 3
< 0.1%
0.0351 4
< 0.1%
0.0352 2
< 0.1%
0.0353 2
< 0.1%
ValueCountFrequency (%)
117.8584 1
 
< 0.1%
116.4568 1
 
< 0.1%
114.818 6
 
< 0.1%
113.4868 7
 
< 0.1%
111.9292 10
 
< 0.1%
110.6632 16
 
< 0.1%
109.181 21
< 0.1%
107.9756 43
< 0.1%
106.5634 30
< 0.1%
105.4143 45
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8206
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.125991
Minimum0.0594
Maximum157.6027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:15.002197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0594
5-th percentile0.1409
Q10.5067
median1.6152
Q334.9088
95-th percentile80.5097
Maximum157.6027
Range157.5433
Interquartile range (IQR)34.4021

Descriptive statistics

Standard deviation28.206296
Coefficient of variation (CV)1.4747626
Kurtosis0.22560085
Mean19.125991
Median Absolute Deviation (MAD)1.4728
Skewness1.2973568
Sum5653968.1
Variance795.59512
MonotonicityNot monotonic
2022-12-20T14:20:15.165324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.5541 1093
 
0.4%
84.278 1084
 
0.4%
79.7171 1083
 
0.4%
81.1822 1072
 
0.4%
85.1632 1052
 
0.4%
85.915 1050
 
0.4%
82.7015 1042
 
0.4%
80.5097 1025
 
0.3%
78.3034 1019
 
0.3%
79.0683 1012
 
0.3%
Other values (8196) 285085
96.4%
ValueCountFrequency (%)
0.0594 1
 
< 0.1%
0.0595 1
 
< 0.1%
0.0607 1
 
< 0.1%
0.0608 1
 
< 0.1%
0.0611 4
< 0.1%
0.0613 1
 
< 0.1%
0.0614 1
 
< 0.1%
0.0617 1
 
< 0.1%
0.0618 1
 
< 0.1%
0.0622 1
 
< 0.1%
ValueCountFrequency (%)
157.6027 1
 
< 0.1%
133.9633 1
 
< 0.1%
128.0195 1
 
< 0.1%
121.0628 2
 
< 0.1%
117.8584 1
 
< 0.1%
116.4568 2
 
< 0.1%
114.818 3
 
< 0.1%
113.4868 14
< 0.1%
111.9292 10
< 0.1%
110.6632 17
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8193
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.098577
Minimum0.0541
Maximum133.1563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:15.342876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0541
5-th percentile0.1115
Q10.6096
median4.6395
Q347.0683
95-th percentile83.0507
Maximum133.1563
Range133.1022
Interquartile range (IQR)46.4587

Descriptive statistics

Standard deviation29.275874
Coefficient of variation (CV)1.2674319
Kurtosis-0.42255806
Mean23.098577
Median Absolute Deviation (MAD)4.5264
Skewness1.0063418
Sum6828331.9
Variance857.07678
MonotonicityNot monotonic
2022-12-20T14:20:15.610065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.2033 1185
 
0.4%
83.7703 1168
 
0.4%
84.6501 1153
 
0.4%
83.0507 1150
 
0.4%
86.3116 1124
 
0.4%
85.3974 1107
 
0.4%
81.51 1098
 
0.4%
87.0883 1054
 
0.4%
80.6932 1034
 
0.3%
80.0247 994
 
0.3%
Other values (8183) 284550
96.3%
ValueCountFrequency (%)
0.0541 1
< 0.1%
0.0546 1
< 0.1%
0.0549 1
< 0.1%
0.055 1
< 0.1%
0.0551 2
< 0.1%
0.0558 1
< 0.1%
0.0559 1
< 0.1%
0.0562 2
< 0.1%
0.0563 1
< 0.1%
0.0564 1
< 0.1%
ValueCountFrequency (%)
133.1563 1
 
< 0.1%
117.1484 1
 
< 0.1%
112.8031 3
 
< 0.1%
111.2549 9
 
< 0.1%
109.9965 29
 
< 0.1%
108.5233 54
< 0.1%
107.3251 53
< 0.1%
105.9214 77
< 0.1%
104.7792 80
< 0.1%
103.4404 84
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7518
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.194587
Minimum0.0388
Maximum102.952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:15.776964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0388
5-th percentile0.1012
Q12.2035
median23.8181
Q337.7935
95-th percentile55.6951
Maximum102.952
Range102.9132
Interquartile range (IQR)35.59

Descriptive statistics

Standard deviation19.14882
Coefficient of variation (CV)0.82557278
Kurtosis-0.93426245
Mean23.194587
Median Absolute Deviation (MAD)17.1414
Skewness0.31553528
Sum6856714.3
Variance366.6773
MonotonicityNot monotonic
2022-12-20T14:20:15.939348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.0062 1114
 
0.4%
38.4023 1104
 
0.4%
39.2192 1098
 
0.4%
38.8066 1095
 
0.4%
36.4987 1094
 
0.4%
36.6641 1094
 
0.4%
41.4185 1089
 
0.4%
39.835 1088
 
0.4%
36.3021 1087
 
0.4%
37.7935 1078
 
0.4%
Other values (7508) 284676
96.3%
ValueCountFrequency (%)
0.0388 1
< 0.1%
0.0392 1
< 0.1%
0.0394 1
< 0.1%
0.0396 1
< 0.1%
0.0397 1
< 0.1%
0.0398 2
< 0.1%
0.04 2
< 0.1%
0.0403 1
< 0.1%
0.0405 1
< 0.1%
0.0406 1
< 0.1%
ValueCountFrequency (%)
102.952 1
 
< 0.1%
94.1082 1
 
< 0.1%
90.6217 1
 
< 0.1%
87.5664 1
 
< 0.1%
86.4726 1
 
< 0.1%
85.5816 4
 
< 0.1%
84.5361 11
< 0.1%
83.6839 13
< 0.1%
82.6836 25
< 0.1%
81.8678 22
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7782
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.078748
Minimum0.0478
Maximum144.3276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:16.102940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0478
5-th percentile0.1146
Q12.0238
median34.6844
Q354.0448
95-th percentile81.8393
Maximum144.3276
Range144.2798
Interquartile range (IQR)52.021

Descriptive statistics

Standard deviation28.199346
Coefficient of variation (CV)0.85249134
Kurtosis-0.99649088
Mean33.078748
Median Absolute Deviation (MAD)26.1375
Skewness0.33310589
Sum9778640.3
Variance795.20313
MonotonicityNot monotonic
2022-12-20T14:20:16.262633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4875 1437
 
0.5%
52.7357 1401
 
0.5%
51.1571 1370
 
0.5%
50.296 1354
 
0.5%
50.8849 1336
 
0.5%
49.4117 1327
 
0.4%
51.7661 1326
 
0.4%
49.1574 1320
 
0.4%
53.3824 1308
 
0.4%
54.7235 1306
 
0.4%
Other values (7772) 282132
95.4%
ValueCountFrequency (%)
0.0478 2
< 0.1%
0.048 1
< 0.1%
0.0482 1
< 0.1%
0.0483 2
< 0.1%
0.0484 1
< 0.1%
0.0485 1
< 0.1%
0.0489 1
< 0.1%
0.049 1
< 0.1%
0.0491 1
< 0.1%
0.0492 1
< 0.1%
ValueCountFrequency (%)
144.3276 1
 
< 0.1%
142.2337 1
 
< 0.1%
133.6943 1
 
< 0.1%
129.7955 1
 
< 0.1%
127.7625 1
 
< 0.1%
126.116 3
 
< 0.1%
124.1949 2
 
< 0.1%
122.6378 8
< 0.1%
120.8197 8
< 0.1%
119.345 9
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7767
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.620371
Minimum0.0473
Maximum175.4801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:16.437483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0473
5-th percentile0.1233
Q11.6735
median24.3489
Q351.1251
95-th percentile79.9474
Maximum175.4801
Range175.4328
Interquartile range (IQR)49.4516

Descriptive statistics

Standard deviation27.8007
Coefficient of variation (CV)0.9385669
Kurtosis-0.90805326
Mean29.620371
Median Absolute Deviation (MAD)24.1451
Skewness0.53116915
Sum8756285.2
Variance772.87889
MonotonicityNot monotonic
2022-12-20T14:20:16.590847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.8369 1175
 
0.4%
50.4951 1173
 
0.4%
53.1112 1165
 
0.4%
49.6055 1165
 
0.4%
50.2138 1164
 
0.4%
51.7707 1164
 
0.4%
49.8802 1149
 
0.4%
51.1251 1144
 
0.4%
49.0116 1135
 
0.4%
52.4326 1133
 
0.4%
Other values (7757) 284050
96.1%
ValueCountFrequency (%)
0.0473 1
< 0.1%
0.0479 1
< 0.1%
0.0482 1
< 0.1%
0.0486 1
< 0.1%
0.0487 1
< 0.1%
0.0489 1
< 0.1%
0.0491 1
< 0.1%
0.0492 1
< 0.1%
0.0495 2
< 0.1%
0.0496 1
< 0.1%
ValueCountFrequency (%)
175.4801 1
 
< 0.1%
150.3595 1
 
< 0.1%
131.5013 1
 
< 0.1%
125.7588 2
 
< 0.1%
116.8232 2
 
< 0.1%
115.3585 7
 
< 0.1%
113.6483 8
 
< 0.1%
112.2611 23
< 0.1%
110.6402 37
< 0.1%
109.3244 52
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7613
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.195349
Minimum0.0532
Maximum134.7263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:16.756841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0532
5-th percentile0.1219
Q12.1587
median34.1972
Q354.3932
95-th percentile82.883
Maximum134.7263
Range134.6731
Interquartile range (IQR)52.2345

Descriptive statistics

Standard deviation28.320643
Coefficient of variation (CV)0.85315093
Kurtosis-1.0621174
Mean33.195349
Median Absolute Deviation (MAD)26.2576
Skewness0.32256287
Sum9813109.6
Variance802.05883
MonotonicityNot monotonic
2022-12-20T14:20:16.948717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.7185 1430
 
0.5%
52.0732 1377
 
0.5%
52.4174 1368
 
0.5%
54.7053 1367
 
0.5%
51.7898 1367
 
0.5%
56.1218 1349
 
0.5%
51.4536 1345
 
0.5%
50.8483 1343
 
0.5%
53.0601 1334
 
0.5%
53.3574 1332
 
0.5%
Other values (7603) 282005
95.4%
ValueCountFrequency (%)
0.0532 1
< 0.1%
0.0533 1
< 0.1%
0.0534 2
< 0.1%
0.0538 1
< 0.1%
0.0539 1
< 0.1%
0.054 1
< 0.1%
0.0543 1
< 0.1%
0.0544 2
< 0.1%
0.0547 2
< 0.1%
0.0549 1
< 0.1%
ValueCountFrequency (%)
134.7263 1
 
< 0.1%
116.9118 2
 
< 0.1%
115.5214 2
 
< 0.1%
113.8957 8
 
< 0.1%
112.5752 13
 
< 0.1%
111.0301 35
 
< 0.1%
109.7743 51
 
< 0.1%
108.304 82
< 0.1%
107.1083 94
< 0.1%
105.7075 136
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6216
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.623366
Minimum0.0334
Maximum112.891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:17.145633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0334
5-th percentile0.0998
Q113.1274
median29.9763
Q344.4719
95-th percentile66.0689
Maximum112.891
Range112.8576
Interquartile range (IQR)31.3445

Descriptive statistics

Standard deviation21.487029
Coefficient of variation (CV)0.72534058
Kurtosis-0.78232883
Mean29.623366
Median Absolute Deviation (MAD)14.8724
Skewness0.14774979
Sum8757170.5
Variance461.69243
MonotonicityNot monotonic
2022-12-20T14:20:17.328740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.5736 1092
 
0.4%
56.4158 1086
 
0.4%
52.6564 1082
 
0.4%
50.3742 1079
 
0.4%
53.7445 1078
 
0.4%
51.311 1076
 
0.4%
54.8097 1076
 
0.4%
54.073 1073
 
0.4%
54.4724 1072
 
0.4%
52.3446 1069
 
0.4%
Other values (6206) 284834
96.4%
ValueCountFrequency (%)
0.0334 1
 
< 0.1%
0.0335 2
< 0.1%
0.0337 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0343 1
 
< 0.1%
0.0345 1
 
< 0.1%
0.0346 1
 
< 0.1%
0.0347 4
< 0.1%
0.0348 1
 
< 0.1%
ValueCountFrequency (%)
112.891 1
 
< 0.1%
105.4803 2
 
< 0.1%
102.8265 1
 
< 0.1%
101.6635 2
 
< 0.1%
100.3018 1
 
< 0.1%
99.1944 2
 
< 0.1%
97.8971 5
< 0.1%
96.8414 5
< 0.1%
95.604 8
< 0.1%
94.5965 12
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6148
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.416521
Minimum0.0292
Maximum109.1693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:17.496961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0292
5-th percentile0.097
Q18.6555
median23.4225
Q339.0446
95-th percentile64.1837
Maximum109.1693
Range109.1401
Interquartile range (IQR)30.3891

Descriptive statistics

Standard deviation20.273079
Coefficient of variation (CV)0.79763391
Kurtosis-0.63739735
Mean25.416521
Median Absolute Deviation (MAD)15.4539
Skewness0.45323028
Sum7513555.8
Variance410.99775
MonotonicityNot monotonic
2022-12-20T14:20:17.656016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0975 3029
 
1.0%
0.0973 2963
 
1.0%
0.0972 2932
 
1.0%
0.0976 2917
 
1.0%
0.0977 2697
 
0.9%
0.0971 2636
 
0.9%
0.097 2487
 
0.8%
0.0978 2375
 
0.8%
0.098 2071
 
0.7%
0.0968 2060
 
0.7%
Other values (6138) 269450
91.1%
ValueCountFrequency (%)
0.0292 1
 
< 0.1%
0.0293 2
< 0.1%
0.0298 2
< 0.1%
0.0299 2
< 0.1%
0.03 1
 
< 0.1%
0.0302 4
< 0.1%
0.0304 2
< 0.1%
0.0306 1
 
< 0.1%
0.0307 4
< 0.1%
0.0308 2
< 0.1%
ValueCountFrequency (%)
109.1693 1
 
< 0.1%
87.7234 2
 
< 0.1%
84.9877 2
 
< 0.1%
84.2149 1
 
< 0.1%
83.3057 1
 
< 0.1%
82.5627 1
 
< 0.1%
81.6883 10
 
< 0.1%
80.9734 13
 
< 0.1%
80.1318 23
< 0.1%
79.4435 48
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6399
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.897309
Minimum0.0374
Maximum122.1443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:17.920342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0374
5-th percentile0.1186
Q18.2831
median25.3118
Q341.8301
95-th percentile64.1416
Maximum122.1443
Range122.1069
Interquartile range (IQR)33.547

Descriptive statistics

Standard deviation21.075719
Coefficient of variation (CV)0.78356238
Kurtosis-0.82243644
Mean26.897309
Median Absolute Deviation (MAD)16.7043
Skewness0.34495186
Sum7951301.7
Variance444.18595
MonotonicityNot monotonic
2022-12-20T14:20:18.085000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1202 2128
 
0.7%
0.1204 2106
 
0.7%
0.1201 1970
 
0.7%
0.1205 1948
 
0.7%
0.1206 1813
 
0.6%
0.1199 1729
 
0.6%
0.1208 1638
 
0.6%
0.1198 1595
 
0.5%
0.1209 1552
 
0.5%
0.1197 1408
 
0.5%
Other values (6389) 277730
93.9%
ValueCountFrequency (%)
0.0374 1
 
< 0.1%
0.0377 1
 
< 0.1%
0.0379 1
 
< 0.1%
0.038 2
< 0.1%
0.0381 4
< 0.1%
0.0384 2
< 0.1%
0.0386 2
< 0.1%
0.0387 1
 
< 0.1%
0.0388 4
< 0.1%
0.039 1
 
< 0.1%
ValueCountFrequency (%)
122.1443 1
 
< 0.1%
104.7792 1
 
< 0.1%
91.7066 1
 
< 0.1%
90.6767 1
 
< 0.1%
89.8357 8
 
< 0.1%
88.8467 10
 
< 0.1%
88.0388 9
 
< 0.1%
87.0883 18
< 0.1%
86.3116 25
< 0.1%
85.3974 21
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6267
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.516124
Minimum0.0313
Maximum102.6604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:18.267546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0313
5-th percentile0.1084
Q111.1672
median29.4858
Q345.0218
95-th percentile66.8349
Maximum102.6604
Range102.6291
Interquartile range (IQR)33.8546

Descriptive statistics

Standard deviation21.818184
Coefficient of variation (CV)0.73919543
Kurtosis-0.84720114
Mean29.516124
Median Absolute Deviation (MAD)15.9272
Skewness0.18417595
Sum8725468.1
Variance476.03315
MonotonicityNot monotonic
2022-12-20T14:20:18.433196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.109 3258
 
1.1%
0.1089 3214
 
1.1%
0.1092 3053
 
1.0%
0.1088 3005
 
1.0%
0.1086 2502
 
0.8%
0.1093 2381
 
0.8%
0.1094 2100
 
0.7%
0.1085 2084
 
0.7%
0.1096 1895
 
0.6%
0.1098 1677
 
0.6%
Other values (6257) 270448
91.5%
ValueCountFrequency (%)
0.0313 1
 
< 0.1%
0.0317 1
 
< 0.1%
0.0318 5
< 0.1%
0.032 5
< 0.1%
0.0323 2
 
< 0.1%
0.0325 8
< 0.1%
0.0326 2
 
< 0.1%
0.0327 1
 
< 0.1%
0.0328 3
 
< 0.1%
0.033 7
< 0.1%
ValueCountFrequency (%)
102.6604 1
 
< 0.1%
99.1078 1
 
< 0.1%
92.8633 3
 
< 0.1%
91.9845 11
 
< 0.1%
90.9514 13
 
< 0.1%
90.1079 22
 
< 0.1%
89.1159 35
< 0.1%
88.3056 43
< 0.1%
87.3522 57
< 0.1%
86.5731 63
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6232
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.358143
Minimum0.0331
Maximum113.3728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:18.600983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.1079
Q110.4208
median27.98
Q341.8191
95-th percentile58.8007
Maximum113.3728
Range113.3397
Interquartile range (IQR)31.3983

Descriptive statistics

Standard deviation19.861775
Coefficient of variation (CV)0.72599134
Kurtosis-0.85391724
Mean27.358143
Median Absolute Deviation (MAD)14.785
Skewness0.11002342
Sum8087532.1
Variance394.49009
MonotonicityNot monotonic
2022-12-20T14:20:18.762073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1094 1388
 
0.5%
0.1095 1324
 
0.4%
0.1093 1323
 
0.4%
0.1096 1305
 
0.4%
0.109 1260
 
0.4%
0.1091 1258
 
0.4%
55.4749 1241
 
0.4%
54.4059 1212
 
0.4%
56.5195 1209
 
0.4%
54.7785 1207
 
0.4%
Other values (6222) 282890
95.7%
ValueCountFrequency (%)
0.0331 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0338 2
< 0.1%
0.0342 2
< 0.1%
0.0343 2
< 0.1%
0.0344 1
 
< 0.1%
0.0346 2
< 0.1%
0.0347 1
 
< 0.1%
0.0348 3
< 0.1%
0.0349 2
< 0.1%
ValueCountFrequency (%)
113.3728 1
 
< 0.1%
89.2954 4
 
< 0.1%
88.4834 4
 
< 0.1%
87.5281 6
 
< 0.1%
86.7475 13
 
< 0.1%
85.8287 39
< 0.1%
85.0777 34
 
< 0.1%
84.1934 62
< 0.1%
83.4702 73
< 0.1%
82.6184 93
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6393
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.983163
Minimum0.0331
Maximum90.1271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:18.931843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.1018
Q18.4775
median24.0542
Q338.0327
95-th percentile58.9429
Maximum90.1271
Range90.094
Interquartile range (IQR)29.5552

Descriptive statistics

Standard deviation19.195789
Coefficient of variation (CV)0.76834904
Kurtosis-0.73640257
Mean24.983163
Median Absolute Deviation (MAD)14.3218
Skewness0.32406718
Sum7385447.6
Variance368.47832
MonotonicityNot monotonic
2022-12-20T14:20:19.087715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1037 1293
 
0.4%
0.1038 1279
 
0.4%
0.1035 1251
 
0.4%
0.1039 1248
 
0.4%
0.1034 1240
 
0.4%
0.104 1231
 
0.4%
0.1032 1212
 
0.4%
0.1042 1105
 
0.4%
0.1031 1105
 
0.4%
0.103 1096
 
0.4%
Other values (6383) 283557
95.9%
ValueCountFrequency (%)
0.0331 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.0339 1
 
< 0.1%
0.0341 3
< 0.1%
0.0345 1
 
< 0.1%
0.0348 1
 
< 0.1%
0.0349 1
 
< 0.1%
0.035 2
< 0.1%
0.0351 2
< 0.1%
0.0352 1
 
< 0.1%
ValueCountFrequency (%)
90.1271 1
 
< 0.1%
87.5052 1
 
< 0.1%
81.016 2
 
< 0.1%
80.2041 8
 
< 0.1%
79.5397 12
 
< 0.1%
78.7567 18
 
< 0.1%
78.1157 27
 
< 0.1%
77.3599 43
< 0.1%
76.7411 60
< 0.1%
76.0113 82
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6184
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.274342
Minimum0.0325
Maximum124.0859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:20:19.247204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0325
5-th percentile0.1074
Q110.3284
median28.8391
Q347.2945
95-th percentile71.2524
Maximum124.0859
Range124.0534
Interquartile range (IQR)36.9661

Descriptive statistics

Standard deviation23.190534
Coefficient of variation (CV)0.7660128
Kurtosis-0.94311424
Mean30.274342
Median Absolute Deviation (MAD)18.4554
Skewness0.2601318
Sum8949610.2
Variance537.80085
MonotonicityNot monotonic
2022-12-20T14:20:19.414642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1079 2809
 
1.0%
0.108 2714
 
0.9%
0.1078 2665
 
0.9%
0.1082 2433
 
0.8%
0.1077 2415
 
0.8%
0.1083 2387
 
0.8%
0.1084 2291
 
0.8%
0.1075 2107
 
0.7%
0.1086 2042
 
0.7%
0.1074 1814
 
0.6%
Other values (6174) 271940
92.0%
ValueCountFrequency (%)
0.0325 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0327 4
< 0.1%
0.0328 1
 
< 0.1%
0.0329 2
 
< 0.1%
0.033 1
 
< 0.1%
0.0332 5
< 0.1%
0.0333 4
< 0.1%
0.0334 5
< 0.1%
0.0336 2
 
< 0.1%
ValueCountFrequency (%)
124.0859 1
 
< 0.1%
120.6638 2
 
< 0.1%
102.1866 1
 
< 0.1%
95.4717 1
 
< 0.1%
92.521 2
 
< 0.1%
90.595 1
 
< 0.1%
89.5776 3
< 0.1%
88.7468 3
< 0.1%
87.7697 5
< 0.1%
86.9716 5
< 0.1%

Interactions

2022-12-20T14:20:06.637184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:57.872966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:01.122692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:04.709390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:08.171482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:11.623394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:15.133380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:19.003175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:22.650911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:26.371558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:30.011886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:33.599872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:37.450595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:41.128453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:44.904004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:48.484076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:52.173033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:55.869264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:59.427726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:03.142565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:06.805224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.033179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:01.286056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:04.862890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:08.335995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:11.784394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:15.300346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:19.169521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:22.819170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:26.537917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:30.178347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:33.774203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:37.604852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:41.300683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:45.078774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:48.657996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:52.335179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:56.036927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:59.605465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:03.307065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:06.996641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.201302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:01.468443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:05.034507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:08.516912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:11.971933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:15.489051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:19.357942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:23.009888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:26.750882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:30.365587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:33.962952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:37.793381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:41.502295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:45.267368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:48.846642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:52.526083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:56.225302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:59.798096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:03.494574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:07.170321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.356953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:01.740804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:05.196109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:08.683948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:12.134249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:15.662215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:19.661813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:23.181812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:26.948345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:30.539098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:34.143586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:37.967482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:41.679889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:45.438907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:49.135504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:52.697428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:56.402938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:59.973444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:03.666737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:07.352479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.521419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:01.910987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:05.366015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:08.857294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:12.306202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:15.846815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:19.829127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:23.368459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:27.152315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:30.718408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:34.326896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:38.143507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:41.868391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:45.626917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:49.303576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:52.891346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:56.584106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:00.158383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:03.839576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:07.529074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.673484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:02.077026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:05.532154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:09.024956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:12.469286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:16.021403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:20.000251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:23.546515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:27.327706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:30.889637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:34.510845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:38.325495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:42.048126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:45.799020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:49.482954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:53.069213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:56.759322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:00.334198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:04.006696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:07.724607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.840465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:02.251157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:05.720272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:09.206604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:12.647069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:16.204347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:20.175172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:23.741026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:27.520667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:31.164886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:34.701302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:38.542617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:42.244951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:45.986891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:49.674152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:53.258267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:56.950998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:00.531023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:04.199908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:07.992550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:58.999177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:02.425532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:05.887067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:09.375598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:12.818499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:16.382556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:20.352645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:23.926545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:27.698781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:31.338925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:34.878820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:38.736424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:42.428115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:46.161659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:49.853056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:53.436799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:57.130787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:00.711206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:04.371501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:08.181515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:59.173168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:02.607377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:06.063207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:09.567402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:19:11.278682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:14.802502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:18.544735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:22.312246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:26.025824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:29.664216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:33.257444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:36.981857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:40.770207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:44.541494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:48.133622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:51.823063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:55.521527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:59.083169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:02.797558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:06.296913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:10.148165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:00.961920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:04.498415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:08.000183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:11.440940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:14.962001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:18.819617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:22.475854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:26.195807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:29.832345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:33.423140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:37.146464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:40.941047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:44.718508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:48.303037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:51.990403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:55.689847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:19:59.251193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:02.966601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:20:06.458764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:20:19.578153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:20:19.849422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:20:20.213983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:20:20.489815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:20:20.765526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:20:10.406951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:20:11.237286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.048.470024.6200247.49260.20000.68310.69921.194016.025523.085511.287219.595353.035547.075355.196263.909051.818249.477563.3641
10.3090.048.470024.6200243.82820.19980.66490.69761.151415.286322.460510.376418.568653.035548.478952.814362.498050.346649.790063.3641
20.6180.048.470024.6200243.06680.20000.64810.68631.113314.392320.81389.874217.752543.762747.369355.516857.903851.539350.053365.8253
30.9280.048.470024.6200242.30300.20000.63180.67571.079514.007419.57729.312716.680153.035546.833051.557863.909052.442249.220162.4461
41.2370.048.470224.6206241.56320.20000.61780.66591.048013.143718.63488.730115.695659.761446.308554.503357.556551.539349.477564.8363
51.5470.048.473324.6330241.21880.19950.60390.65681.019812.774717.70438.199414.704553.355545.520151.557862.989049.461448.662862.4461
61.8560.048.476324.6454240.87550.20000.59160.64810.991511.893716.64267.769213.846858.423545.795353.827357.903851.539349.220164.8363
72.1660.048.479424.6578240.53110.20000.13960.21400.17540.43840.38790.29050.26783.18670.76890.94840.31510.24530.20550.1775
82.4740.048.480024.6600240.68430.86740.06230.15560.11230.12180.12930.14790.12840.14700.11540.15010.12880.13180.11890.1186
92.7840.048.480024.6600240.94690.88930.06840.14890.10780.10650.11400.13490.12080.11800.10360.12810.11840.12190.11220.1134
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29560790906.9900.063.939524.620.22160.89900.09500.15110.12840.10840.12550.13010.13440.11060.09980.12340.11120.11550.10980.1104
29560890907.2990.063.940024.620.00000.89900.09570.15200.12960.10900.12650.13060.13540.11070.09990.12340.11100.11550.10990.1104
29560990907.6080.063.940024.620.00000.89880.09650.15300.13040.10950.12750.13120.07590.11070.09960.12370.11090.04080.06440.0680
29561090907.9170.063.940024.620.00000.89900.14880.41530.63330.45600.71770.83621.21680.59400.64390.93801.66012.93944.19605.1170
29561190908.2270.063.940024.620.00000.21301.70894.21706.40144.31456.17366.96069.487017.888916.091518.865626.182731.756832.877041.1046
29561290908.5340.063.940024.620.00000.20809.932219.157830.122517.470528.224129.284835.202554.472451.781359.302363.403961.348453.804465.2822
29561390908.8430.063.940024.620.00000.205031.788750.663862.309232.033454.351257.211864.529278.237263.738869.222868.350670.690962.418174.9537
29561490909.1510.063.940024.620.00000.204057.730476.938380.693238.993182.535474.225877.053153.035570.686975.873476.103371.316061.058973.6785
29561590909.4610.063.940024.620.00000.202071.917682.004086.311640.713479.557183.910786.137282.115967.280771.484468.350674.269862.418174.9537
29561690909.7700.063.940024.620.00000.201061.803582.701587.088342.588983.386381.516579.076871.359967.875772.670776.103368.488261.058971.7899